@thesis{thesis,
		author={Fitriani  Yessy and Prathama  Muhammad Fadli and TUPAMAHU  MELDRIN},
		title ={“KLASTERISASI DATA HISTORICAL PENGGUNAAN DAYA PELANGGAN AMR (AUTOMATIC METER READING) MENGGUNAKAN METODE K-MEANS PADA PT. PLN(PERSERO) DISTRIBUSI JAKARTA RAYA”},
		year={2018},
		url={http://156.67.221.169/4647/},
		abstract={AMR System (Automatic Meter Reading) PT. PLN (Persero) Distribusi Jakarta
Raya applied to be able to detect losses (loss of electric power).Non-technical
losses is one type of losses that gives a big effect on power loss. Currently to
detect the losses itself is still checked directly by reading and checking historical
power usage data of every incoming customer. For that we need a system that
can be used to make data analysis and evaluation easier. This research uses
Clustering K-means method to classify data based on electric power usage and
Davies-Bouldin Index method to determine which cluster set is most optimal for
use in grouping. This application is designed using a flowchart to describe each
process performed within the application. For functional testing in applications
using black-box method. Test results on the grouping of AMR's historical power
usage data show that K-Means method is able to classify the customer's
electricity usage with the optimal number of cluster sets, 3 based on the optimal
cluster set using Davies-Bouldin Index. With this application is expected result of
power usage pattern obtained, can be used as a reference in analyzing every
usage graph from AMR Customer historical power usage data and can detect
non-technical losses customer error.}
		}